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G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

Research Medical/Healthcare AI

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Representative image for G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

Merged summary

TL;DR - G-CARL is a reinforcement-learning framework for generating accurate, patient-friendly explanations of medical reports from user queries and dialogue history. It matters because it jointly targets verifiable medical factuality and personalized communication without forcing standardized responses.

  • Introduces Patient-oriented Medical Report Interpretation, an open-ended multimodal generation task combining report evidence, user questions, and dialogue context.
  • Uses multi-source retrieval to verify atomic medical claims and instance-specific weighted checklists to assess response coverage.
  • Provides structured rewards for factuality, user-demand satisfaction, and expression quality while preserving response diversity.
  • Introduces the real-world MMedReport benchmark; experiments and clinician preference evaluations show improvements over existing post-training baselines in overall quality, claim precision, and checklist recall.

Sources (1)

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

arXiv cs.CL Shiao Xie, Siyu Chen, Jianwei Lv, Bo Yuan, Yujin Wang, Xiandong Li 2026-08-20 arXiv:2608.20331
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-19 14:24:49.671985 UTC

TL;DR - G-CARL is a reinforcement-learning framework for generating accurate, patient-friendly explanations of medical reports from user queries and dialogue history. It matters because it jointly targets verifiable medical factuality and personalized communication without forcing standardized responses.

  • Introduces Patient-oriented Medical Report Interpretation, an open-ended multimodal generation task combining report evidence, user questions, and dialogue context.
  • Uses multi-source retrieval to verify atomic medical claims and instance-specific weighted checklists to assess response coverage.
  • Provides structured rewards for factuality, user-demand satisfaction, and expression quality while preserving response diversity.
  • Introduces the real-world MMedReport benchmark; experiments and clinician preference evaluations show improvements over existing post-training baselines in overall quality, claim precision, and checklist recall.
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